# IMPORT LIBRARIES AND DATASETS
# Import key librares and modules 
import pandas as pd
import numpy as np

# Import datetime module that comes pre-installed in Python 
# datetime offers classes that work with date & time information
import datetime as dt

# Use Pandas to read stock data
stock_amzn = pd.read_csv('AMZN01.csv')
stock_amzn.head(241)

# Count the number of missing values in "stock_amzn" Pandas DataFrame
stock_amzn.isnull().sum()

Date         0
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64

# Obtain information about the Pandas DataFrame such as data types, memory utilization..etc
stock_amzn.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 241 entries, 0 to 240
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       241 non-null    object 
 1   Open       241 non-null    float64
 2   High       241 non-null    float64
 3   Low        241 non-null    float64
 4   Close      241 non-null    float64
 5   Adj Close  241 non-null    float64
 6   Volume     241 non-null    int64  
dtypes: float64(5), int64(1), object(1)
memory usage: 13.3+ KB

# Calculate the percentage monthly return
stock_amzn['Monthly Return'] = stock_amzn['Adj Close'].pct_change(1) * 100
stock_amzn

# We have noticed "NaN" in the first row. Let's replace the first row with zeros instead of NaN
stock_amzn.replace({'Daily Return': 0}, inplace = True)
stock_amzn

# Use the describe() method to obtain a statistical summary about the data 
# Over the specified time period, the average adjusted close price for Amazon stock was $51.159 
# The maximum adjusted close price was $195.05
# The maximum volume of shares traded on one day were 32,76,182,000
stock_amzn.describe().round(2)

# Matplotlib is a comprehensive data visualization library in Python 
# Seaborn is a visualization library that sits on top of matplotlib and offers enhanced features 
# plotly.express module contains functions that can create interactive figures using a few lines of code

import matplotlib.pyplot as plt

!pip install seaborn
import seaborn as sns

import plotly.express as px

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# Let's plot a Line Plot Using Plotly Express
fig = px.line(title = 'Amazon.com, Inc. (AMZN) Adjusted Closing Price [$]')
fig.add_scatter(x = stock_amzn['Date'], y = stock_amzn['Adj Close'], name = 'Adj Close')
stock_amzn

# This function coded below performs interactive data visualization using Plotly Express
def plot_financial_data(amzn, title):
    
    fig = px.line(title = title)
    
    # For loop that plots all stock prices in the pandas dataframe pd
    # Note that index starts with 1 because we want to skip the date column
    
    for i in amzn.columns[1:]:
        fig.add_scatter(x = amzn['Date'], y = amzn[i], name = i)
        fig.update_traces(line_width = 5)
        fig.update_layout({'plot_bgcolor': "white"})

    fig.show()

# Plot High, Low, Open, Close and Adj Close
plot_financial_data(stock_amzn.drop(['Volume', 'Monthly Return'], axis = 1), 'Amazon.com, Inc. (AMZN) Stock Price [$]')

# Plot trading volume
plot_financial_data(stock_amzn.iloc[:,[0,5]], 'Amazon.com, Inc. (AMZN) Trading Volume')

# Plot % Monthly Returns
plot_financial_data(stock_amzn.iloc[:,[0,7]], 'Amazon.com, Inc. (AMZN) Percentage Monthly Return [%]')

# Plot the stocks monthly returns
plot_financial_data(stock_data, 'Percentage Monthly Returns [%]')

import pandas as pd
import yfinance as yf  # Import yfinance library for stock data
import numpy as np

# Define a list of stock tickers we are interested in.
tickers = ['ADBE', 'AMZN', 'CAT', 'DE', 'EXC', 'JNJ', 'JPM', 'MSFT', 'PFE', 'PG']

# Create an empty DataFrame to store adjusted close prices
adj_close_data = pd.DataFrame()

# Download the adjusted close prices for each stock
for ticker in tickers:
    # Fetch the data from Yahoo Finance
    data = yf.download(ticker, start='2004-01-01', end='2024-01-01', progress=False)
    
    # Select only the Adjusted Close prices and rename the column to the ticker symbol
    adj_close_data[ticker] = data['Adj Close']

print("Adjusted Close Prices DataFrame:")
print(adj_close_data.head())

# Calculate the percentage monthly returns
monthly_returns_df = adj_close_data.resample('M').ffill().pct_change() * 100  # Resample to monthly frequency, forward-fill missing data, calculate percentage change

# Replace NaN values with 0 (if any, but there shouldn't be any after pct_change)
monthly_returns_df.replace(np.nan, 0, inplace=True)

print("\nMonthly Returns DataFrame:")
print(monthly_returns_df.head())

Adjusted Close Prices DataFrame:
                 ADBE    AMZN        CAT         DE        EXC        JNJ  \
Date                                                                        
2004-01-02  19.518396  2.5950  24.635588  21.555693  10.938865  29.117960   
2004-01-05  19.867832  2.6635  25.306253  21.585871  10.988595  29.287048   
2004-01-06  19.887802  2.6515  24.859148  20.985796  10.909038  29.157417   
2004-01-07  19.694548  2.5950  24.587902  21.522177  10.975337  29.168690   
2004-01-08  18.975481  2.5120  24.397137  21.367962  10.976991  29.303968   

                  JPM       MSFT        PFE         PG  
Date                                                    
2004-01-02  20.959133  17.105961  15.121106  28.238073  
2004-01-05  20.919071  17.535954  15.525189  28.377855  
2004-01-06  21.445629  17.598263  15.516676  28.235224  
2004-01-07  21.760416  17.579573  15.648537  27.984182  
2004-01-08  22.132431  17.548412  15.503916  28.278000  

Monthly Returns DataFrame:
                ADBE       AMZN       CAT        DE       EXC       JNJ  \
Date                                                                      
2004-01-31  0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   
2004-02-29 -2.741515 -14.662695 -3.046225  2.603812  1.072320  1.362429   
2004-03-31  5.537608   0.627760  4.382843  8.353766  2.576653 -5.917244   
2004-04-30  5.597969   0.739373 -1.244059 -1.832327 -2.802329  6.526017   
2004-05-31  7.542179  11.238527 -3.061856 -3.439193  0.365505  3.647175   

                 JPM      MSFT       PFE        PG  
Date                                                
2004-01-31  0.000000  0.000000  0.000000  0.000000  
2004-02-29  5.476989 -4.050699  0.503663  1.414769  
2004-03-31  2.267181 -6.030919 -4.365555  2.311918  
2004-04-30 -9.642520  4.813523  2.025696  1.352106  
2004-05-31 -2.021284  0.382667 -0.704461  1.918878

C:\Users\ok\AppData\Local\Temp\ipykernel_1956\1710146094.py:23: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  monthly_returns_df = adj_close_data.resample('M').ffill().pct_change() * 100  # Resample to monthly frequency, forward-fill missing data, calculate percentage change

# Basic Analysis
# Calculate and print the average monthly return for each stock
average_monthly_returns = monthly_returns_df.mean()
print("\nAverage Monthly Returns:")
print(average_monthly_returns)

# Calculate and print the standard deviation of the monthly returns for each stock
std_deviation_monthly_returns = monthly_returns_df.std()
print("\nStandard Deviation of Monthly Returns:")
print(std_deviation_monthly_returns)

Average Monthly Returns:
ADBE    1.832078
AMZN    2.271867
CAT     1.476629
DE      1.569282
EXC     0.631864
JNJ     0.771122
JPM     1.148342
MSFT    1.498255
PFE     0.424252
PG      0.771014
dtype: float64

Standard Deviation of Monthly Returns:
ADBE     8.742673
AMZN    10.639783
CAT      9.142596
DE       8.218994
EXC      5.450420
JNJ      4.204065
JPM      7.789727
MSFT     6.490790
PFE      6.052442
PG       4.457197
dtype: float64

import matplotlib.pyplot as plt
import seaborn as sns

# Plot the percentage monthly returns
plt.figure(figsize=(12, 8))
monthly_returns_df.plot(title='Percentage Monthly Returns for Stocks', xlabel='Date', ylabel='Monthly Return (%)', marker='o')
plt.legend(tickers, loc='best')
plt.show()

# Plot the heatmap of the correlation matrix
plt.figure(figsize=(10, 8))
sns.heatmap(monthly_returns_df.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1, center=0)
plt.title('Correlation Heatmap of Monthly Returns')
plt.show()

<Figure size 1200x800 with 0 Axes>

adj_close_data.reset_index(inplace=True)

monthly_returns_df = adj_close_data.set_index('Date').resample('M').ffill().pct_change() * 100  # Resample to monthly frequency, forward-fill missing data, calculate percentage change
monthly_returns_df.replace(np.nan, 0, inplace=True)  # Replace NaN values with 0

C:\Users\ok\AppData\Local\Temp\ipykernel_1956\2594310399.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  monthly_returns_df = adj_close_data.set_index('Date').resample('M').ffill().pct_change() * 100  # Resample to monthly frequency, forward-fill missing data, calculate percentage change

monthly_returns_df()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[9], line 1
----> 1 monthly_returns_df()

TypeError: 'DataFrame' object is not callable

monthly_returns_df

# Insert the Date column at the start of the monthly_returns_df DataFrame
monthly_returns_df.insert(0, 'Date', monthly_returns_df.index)  # Use the index as the Date

# Define a function that performs interactive data visualization using Plotly Express
import plotly.express as px 
def plot_financial_data(df, title):
    
    fig = px.line(title = title)
    
    # For loop that plots all stock prices in the pandas dataframe df
    # Note that index starts with 1 because we want to skip the date column
    
    for i in df.columns[1:]:
        fig.add_scatter(x = df['Date'], y = df[i], name = i)
        fig.update_traces(line_width = 5)
        fig.update_layout({'plot_bgcolor': "white"})

    fig.show()

# Plot closing prices using plotly Express. Note that we used the same pre-defined function "plot_financial_data"
plot_financial_data(monthly_returns_df, 'Adjusted Closing Prices [$]')

# Plot the stocks daily returns
plot_financial_data(monthly_returns_df, 'Percentage Monthy Returns [%]')

# Plot histograms for stocks monthly returns using plotly express
fig = px.histogram(monthly_returns_df.drop(columns = ['Date']))
fig.update_layout({'plot_bgcolor': "white"})

# Plot a heatmap showing the correlations between monthly returns
# Strong positive correlations between Catterpillar and John Deere - both into heavy equipment and machinery
# MSFT and Google - both into Tech and Cloud Computing
plt.figure(figsize = (10, 8))
sns.heatmap(monthly_returns_df.drop(columns = ['Date']).corr(), annot = True);

# Plot the Pairplot between stocks monthly returns
sns.pairplot(monthly_returns_df);

# Function to scale stock prices based on their initial starting price
# The objective of this function is to set all prices to start at a value of 1 
def price_scaling(raw_prices_df):
    scaled_prices_df = raw_prices_df.copy()
    for i in raw_prices_df.columns[1:]:
          scaled_prices_df[i] = raw_prices_df[i]/raw_prices_df[i][0]
    return scaled_prices_df

price_scaling(adj_close_data)

# Let's create an array that holds random portfolio weights
# Note that portfolio weights must add up to 1 
import random

def generate_portfolio_weights(n):
    weights = []
    for i in range(n):
        weights.append(random.random())
        
    # let's make the sum of all weights add up to 1
    weights = weights/np.sum(weights)
    return weights

# Call the function (Run this cell multiple times to generate different outputs)
weights = generate_portfolio_weights(10)
print(weights)

[0.07813861 0.1442829  0.04616586 0.14572471 0.00682053 0.1134458
 0.13514778 0.1435415  0.08587131 0.10086099]

# Let's define the "weights" list similar to the slides
weights = [0.07813861, 0.1442829,  0.04616586, 0.14572471, 0.00682053, 0.1134458, 0.13514778, 0.1435415,  0.08587131, 0.10086099]
weights

[0.07813861,
 0.1442829,
 0.04616586,
 0.14572471,
 0.00682053,
 0.1134458,
 0.13514778,
 0.1435415,
 0.08587131,
 0.10086099]

# Let's display "adj_close_data" Pandas DataFrame
adj_close_data

# Scale stock prices using the "price_scaling" function that we defined earlier (make all stock values start at 1)
portfolio_df = adj_close_data.copy()
scaled_df = price_scaling(portfolio_df)
scaled_df

# Use enumerate() method to obtain the stock names along with a counter "i" (0, 1, 2, 3,..etc.)
# This counter "i" will be used as an index to access elements in the "weights" list
initial_investment = 10000000
for i, stock in enumerate(scaled_df.columns[1:]):
    portfolio_df[stock] = weights[i] * scaled_df[stock]  * initial_investment
portfolio_df.round(2)

# Assume that we have $10,000,000 that we would like to invest in one or more of the selected stocks
# Let's create a function that receives the following arguments: 
      # (1) Stocks closing prices
      # (2) Random weights 
      # (3) Initial investment amount
# The function will return a DataFrame that contains the following:
      # (1) Daily value (position) of each individual stock over the specified time period
      # (2) Total daily value of the portfolio 
      # (3) Percentage daily return 

def asset_allocation(df, weights, initial_investment):
    portfolio_df = df.copy()

    # Scale stock prices using the "price_scaling" function that we defined earlier (Make them all start at 1)
    scaled_df = price_scaling(df)
  
    for i, stock in enumerate(scaled_df.columns[1:]):
        portfolio_df[stock] = scaled_df[stock] * weights[i] * initial_investment

    # Sum up all values and place the result in a new column titled "portfolio value [$]" 
    # Note that we excluded the date column from this calculation
    portfolio_df['Portfolio Value [$]'] = portfolio_df[portfolio_df != 'Date'].sum(axis = 1, numeric_only = True)
            
    # Calculate the portfolio percentage monthly return and replace NaNs with zeros
    portfolio_df['Portfolio monthly Return [%]'] = portfolio_df['Portfolio Value [$]'].pct_change(1) * 100 
    portfolio_df.replace(np.nan, 0, inplace = True)
    
    return portfolio_df

portfolio_df

# Now let's put this code in a function and generate random weights
# Let's obtain the number of stocks under consideration (note that we ignored the "Date" column) 
n = len(adj_close_data.columns)-1

# Let's generate random weights 
print('Number of stocks under consideration = {}'.format(n))
weights = generate_portfolio_weights(n).round(6)
print('Portfolio weights = {}'.format(weights))

# Let's test out the "asset_allocation" function
portfolio_df = asset_allocation(adj_close_data, weights, 10000000)
portfolio_df.round(2)

Number of stocks under consideration = 10
Portfolio weights = [0.130612 0.140131 0.069716 0.137146 0.105677 0.035927 0.144462 0.084744
 0.068839 0.082745]

# Plot the portfolio percentage monthly return
plot_financial_data(portfolio_df[['Date', 'Portfolio Daily Return [%]']], 'Portfolio Percentage monthly Return [%]')

# Plot each stock position in our portfolio over time
# This graph shows how our initial investment in each individual stock grows over time
plot_financial_data(portfolio_df.drop(['Portfolio Value [$]', 'Portfolio Daily Return [%]'], axis = 1), 'Portfolio positions [$]')

# Plot the total daily value of the portfolio (sum of all positions)
plot_financial_data(portfolio_df[['Date', 'Portfolio Value [$]']], 'Total Portfolio Value [$]')

	Date	Open	High	Low	Close	Adj Close	Volume
0	2004-08-01	1.922500	2.041500	1.742500	1.907000	1.907000	3600448000
1	2004-09-01	1.911500	2.214000	1.868000	2.043000	2.043000	3523538000
2	2004-10-01	2.064500	2.135000	1.650000	1.706500	1.706500	4171368000
3	2004-11-01	1.711000	2.075000	1.707500	1.984000	1.984000	3369498000
4	2004-12-01	2.000000	2.284000	1.899500	2.214500	2.214500	3520296000
...	...	...	...	...	...	...	...
236	2024-04-01	180.789993	189.770004	166.320007	175.000000	175.000000	917021100
237	2024-05-01	181.639999	191.699997	173.869995	176.440002	176.440002	892301700
238	2024-06-01	177.699997	199.839996	175.919998	193.250000	193.250000	813276000
239	2024-07-01	193.490005	201.199997	192.820007	195.050003	195.050003	303090400
240	2024-07-11	200.089996	200.269897	192.860001	195.050003	195.050003	43773883

	Date	Open	High	Low	Close	Adj Close	Volume	Monthly Return
0	2004-08-01	1.922500	2.041500	1.742500	1.907000	1.907000	3600448000	NaN
1	2004-09-01	1.911500	2.214000	1.868000	2.043000	2.043000	3523538000	7.131620
2	2004-10-01	2.064500	2.135000	1.650000	1.706500	1.706500	4171368000	-16.470876
3	2004-11-01	1.711000	2.075000	1.707500	1.984000	1.984000	3369498000	16.261354
4	2004-12-01	2.000000	2.284000	1.899500	2.214500	2.214500	3520296000	11.617944
...	...	...	...	...	...	...	...	...
236	2024-04-01	180.789993	189.770004	166.320007	175.000000	175.000000	917021100	-2.982595
237	2024-05-01	181.639999	191.699997	173.869995	176.440002	176.440002	892301700	0.822858
238	2024-06-01	177.699997	199.839996	175.919998	193.250000	193.250000	813276000	9.527317
239	2024-07-01	193.490005	201.199997	192.820007	195.050003	195.050003	303090400	0.931438
240	2024-07-11	200.089996	200.269897	192.860001	195.050003	195.050003	43773883	0.000000

	Date	Open	High	Low	Close	Adj Close	Volume	Monthly Return
0	2004-08-01	1.922500	2.041500	1.742500	1.907000	1.907000	3600448000	0.000000
1	2004-09-01	1.911500	2.214000	1.868000	2.043000	2.043000	3523538000	7.131620
2	2004-10-01	2.064500	2.135000	1.650000	1.706500	1.706500	4171368000	-16.470876
3	2004-11-01	1.711000	2.075000	1.707500	1.984000	1.984000	3369498000	16.261354
4	2004-12-01	2.000000	2.284000	1.899500	2.214500	2.214500	3520296000	11.617944
...	...	...	...	...	...	...	...	...
236	2024-04-01	180.789993	189.770004	166.320007	175.000000	175.000000	917021100	-2.982595
237	2024-05-01	181.639999	191.699997	173.869995	176.440002	176.440002	892301700	0.822858
238	2024-06-01	177.699997	199.839996	175.919998	193.250000	193.250000	813276000	9.527317
239	2024-07-01	193.490005	201.199997	192.820007	195.050003	195.050003	303090400	0.931438
240	2024-07-11	200.089996	200.269897	192.860001	195.050003	195.050003	43773883	0.000000

	Open	High	Low	Close	Adj Close	Volume	Monthly Return
count	241.00	241.00	241.00	241.00	241.00	2.410000e+02	241.00
mean	50.52	54.24	47.31	51.16	51.16	2.248021e+09	2.46
std	57.87	61.83	54.30	58.22	58.22	1.150596e+09	10.38
min	1.33	1.55	1.29	1.34	1.34	4.377388e+07	-30.48
25%	4.62	4.87	4.16	4.66	4.66	1.432892e+09	-4.30
50%	17.00	18.29	15.88	17.73	17.73	1.888910e+09	2.30
75%	90.22	97.82	85.87	92.39	92.39	2.843062e+09	8.23
max	200.09	201.20	192.86	195.05	195.05	6.925740e+09	54.13

	Date	Open	High	Low	Close	Adj Close	Volume	Monthly Return
0	2004-08-01	1.922500	2.041500	1.742500	1.907000	1.907000	3600448000	0.000000
1	2004-09-01	1.911500	2.214000	1.868000	2.043000	2.043000	3523538000	7.131620
2	2004-10-01	2.064500	2.135000	1.650000	1.706500	1.706500	4171368000	-16.470876
3	2004-11-01	1.711000	2.075000	1.707500	1.984000	1.984000	3369498000	16.261354
4	2004-12-01	2.000000	2.284000	1.899500	2.214500	2.214500	3520296000	11.617944
...	...	...	...	...	...	...	...	...
236	2024-04-01	180.789993	189.770004	166.320007	175.000000	175.000000	917021100	-2.982595
237	2024-05-01	181.639999	191.699997	173.869995	176.440002	176.440002	892301700	0.822858
238	2024-06-01	177.699997	199.839996	175.919998	193.250000	193.250000	813276000	9.527317
239	2024-07-01	193.490005	201.199997	192.820007	195.050003	195.050003	303090400	0.931438
240	2024-07-11	200.089996	200.269897	192.860001	195.050003	195.050003	43773883	0.000000

	ADBE	AMZN	CAT	DE	EXC	JNJ	JPM	MSFT	PFE	PG
Date
2004-01-31	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
2004-02-29	-2.741515	-14.662695	-3.046225	2.603812	1.072320	1.362429	5.476989	-4.050699	0.503663	1.414769
2004-03-31	5.537608	0.627760	4.382843	8.353766	2.576653	-5.917244	2.267181	-6.030919	-4.365555	2.311918
2004-04-30	5.597969	0.739373	-1.244059	-1.832327	-2.802329	6.526017	-9.642520	4.813523	2.025696	1.352106
2004-05-31	7.542179	11.238527	-3.061856	-3.439193	0.365505	3.647175	-2.021284	0.382667	-0.704461	1.918878
...	...	...	...	...	...	...	...	...	...	...
2023-08-31	2.411345	3.239080	6.018774	-4.343588	-3.297341	-2.791216	-7.362619	-2.222562	-1.885761	-1.254008
2023-09-30	-8.838994	-7.890727	-2.891901	-7.842950	-5.807569	-3.667743	-0.895250	-3.664287	-6.246478	-5.494355
2023-10-31	4.345951	4.696345	-16.769947	-3.185119	3.043130	-4.757630	-3.402213	7.081556	-7.868551	3.508122
2023-11-30	14.838554	9.767827	10.913513	-0.260012	-0.178091	5.095414	12.239306	12.294541	1.049772	2.326201
2023-12-31	-2.358396	4.004385	17.928364	10.133882	-6.777457	1.344897	8.982584	-0.757439	-5.513621	-4.546648

	Date	ADBE	AMZN	CAT	DE	EXC	JNJ	JPM	MSFT	PFE	PG
0	2004-01-02	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	2004-01-05	1.017903	1.026397	1.027223	1.001400	1.004546	1.005807	0.998089	1.025137	1.026723	1.004950
2	2004-01-06	1.018926	1.021773	1.009075	0.973562	0.997273	1.001355	1.023212	1.028780	1.026160	0.999899
3	2004-01-07	1.009025	1.000000	0.998064	0.998445	1.003334	1.001742	1.038231	1.027687	1.034880	0.991009
4	2004-01-08	0.972184	0.968015	0.990321	0.991291	1.003485	1.006388	1.055980	1.025865	1.025316	1.001414
...	...	...	...	...	...	...	...	...	...	...	...
5028	2023-12-22	30.676188	59.121386	11.689238	18.202732	3.157575	5.256253	7.848499	21.817856	1.822835	5.079738
5029	2023-12-26	30.651084	59.117534	11.901397	18.406860	3.181754	5.279244	7.894915	21.822518	1.823477	5.102816
5030	2023-12-27	30.539395	59.090557	12.001639	18.423833	3.171008	5.286345	7.942268	21.788152	1.836314	5.107011
5031	2023-12-28	30.510704	59.105974	11.951719	18.381937	3.210411	5.294121	7.984465	21.858628	1.847867	5.095472
5032	2023-12-29	30.566034	58.551060	11.903007	18.410020	3.214888	5.299531	7.975088	21.902896	1.847867	5.123794

	Date	ADBE	AMZN	CAT	DE	EXC	JNJ	JPM	MSFT	PFE	PG
0	2004-01-02	19.518396	2.595000	24.635588	21.555693	10.938865	29.117960	20.959133	17.105961	15.121106	28.238073
1	2004-01-05	19.867832	2.663500	25.306253	21.585871	10.988595	29.287048	20.919071	17.535954	15.525189	28.377855
2	2004-01-06	19.887802	2.651500	24.859148	20.985796	10.909038	29.157417	21.445629	17.598263	15.516676	28.235224
3	2004-01-07	19.694548	2.595000	24.587902	21.522177	10.975337	29.168690	21.760416	17.579573	15.648537	27.984182
4	2004-01-08	18.975481	2.512000	24.397137	21.367962	10.976991	29.303968	22.132431	17.548412	15.503916	28.278000
...	...	...	...	...	...	...	...	...	...	...	...
5028	2023-12-22	598.750000	153.419998	287.971252	392.372498	34.540291	153.051361	164.497726	373.215393	27.563284	143.442017
5029	2023-12-26	598.260010	153.410004	293.197906	396.772614	34.804779	153.720825	165.470566	373.295135	27.572990	144.093689
5030	2023-12-27	596.080017	153.339996	295.667419	397.138489	34.687229	153.927582	166.463058	372.707275	27.767097	144.212158
5031	2023-12-28	595.520020	153.380005	294.437622	396.235382	35.118248	154.154007	167.347473	373.912842	27.941795	143.886322
5032	2023-12-29	596.599976	151.940002	293.237579	396.840729	35.167229	154.311539	167.150925	374.670074	27.941795	144.686081

	Date	ADBE	AMZN	CAT	DE	EXC	JNJ	JPM	MSFT	PFE	PG
0	2004-01-02	781386.10	1442829.00	461658.60	1457247.10	68205.30	1134458.00	1351477.80	1435415.00	858713.10	1008609.90
1	2004-01-05	795375.17	1480915.26	474226.54	1459287.25	68515.38	1141045.81	1348894.54	1471497.04	881660.60	1013602.64
2	2004-01-06	796174.64	1474243.17	465848.01	1418719.91	68019.32	1135995.29	1382847.82	1476725.60	881177.13	1008508.12
3	2004-01-07	788438.02	1442829.00	460765.00	1454981.30	68432.71	1136434.47	1403145.78	1475157.26	888665.37	999541.40
4	2004-01-08	759651.40	1396680.74	457190.15	1444555.78	68443.02	1141705.03	1427133.89	1472542.50	880452.49	1010036.00
...	...	...	...	...	...	...	...	...	...	...	...
5028	2023-12-22	23969947.03	85302050.13	5396437.34	26525878.47	215363.38	5962998.13	10607071.57	31317677.99	1565292.43	5123474.12
5029	2023-12-26	23950331.10	85296493.15	5494382.22	26823343.10	217012.50	5989080.97	10669801.78	31324369.43	1565843.65	5146750.61
5030	2023-12-27	23863058.77	85257568.84	5540659.66	26848077.67	216279.56	5997136.36	10733799.27	31275040.23	1576866.81	5150982.10
5031	2023-12-28	23840640.21	85279813.73	5517613.87	26787024.21	218967.02	6005958.06	10790827.71	31376203.10	1586787.73	5139343.86
5032	2023-12-29	23883874.43	84479167.39	5495125.67	26827947.95	219272.42	6012095.62	10778153.96	31439744.88	1586787.73	5167909.72

	Date	ADBE	AMZN	CAT	DE	EXC	JNJ	JPM	MSFT	PFE	PG
0	2004-01-02	7.813861e+05	1.442829e+06	4.616586e+05	1.457247e+06	68205.300000	1.134458e+06	1.351478e+06	1.435415e+06	8.587131e+05	1.008610e+06
1	2004-01-05	7.953752e+05	1.480915e+06	4.742265e+05	1.459287e+06	68515.375148	1.141046e+06	1.348895e+06	1.471497e+06	8.816606e+05	1.013603e+06
2	2004-01-06	7.961746e+05	1.474243e+06	4.658480e+05	1.418720e+06	68019.323888	1.135995e+06	1.382848e+06	1.476726e+06	8.811771e+05	1.008508e+06
3	2004-01-07	7.884380e+05	1.442829e+06	4.607650e+05	1.454981e+06	68432.709848	1.136434e+06	1.403146e+06	1.475157e+06	8.886654e+05	9.995414e+05
4	2004-01-08	7.596514e+05	1.396681e+06	4.571901e+05	1.444556e+06	68443.020712	1.141705e+06	1.427134e+06	1.472542e+06	8.804525e+05	1.010036e+06
...	...	...	...	...	...	...	...	...	...	...	...
5028	2023-12-22	2.396995e+07	8.530205e+07	5.396437e+06	2.652588e+07	215363.381961	5.962998e+06	1.060707e+07	3.131768e+07	1.565292e+06	5.123474e+06
5029	2023-12-26	2.395033e+07	8.529649e+07	5.494382e+06	2.682334e+07	217012.501764	5.989081e+06	1.066980e+07	3.132437e+07	1.565844e+06	5.146751e+06
5030	2023-12-27	2.386306e+07	8.525757e+07	5.540660e+06	2.684808e+07	216279.562272	5.997136e+06	1.073380e+07	3.127504e+07	1.576867e+06	5.150982e+06
5031	2023-12-28	2.384064e+07	8.527981e+07	5.517614e+06	2.678702e+07	218967.022931	6.005958e+06	1.079083e+07	3.137620e+07	1.586788e+06	5.139344e+06
5032	2023-12-29	2.388387e+07	8.447917e+07	5.495126e+06	2.682795e+07	219272.424297	6.012096e+06	1.077815e+07	3.143974e+07	1.586788e+06	5.167910e+06

	Date	ADBE	AMZN	CAT	DE	EXC	JNJ	JPM	MSFT	PFE	PG	Portfolio Value [$]	Portfolio Daily Return [%]
0	2004-01-02	1306120.00	1401310.00	697160.00	1371460.00	1056770.00	359270.00	1444620.00	847440.00	688390.00	827450.00	9.999990e+06	0.00
1	2004-01-05	1329503.33	1438300.29	716139.10	1373380.05	1061574.29	361356.29	1441858.71	868742.11	706785.93	831545.98	1.012919e+07	1.29
2	2004-01-06	1330839.67	1431820.19	703486.51	1335200.88	1053888.49	359756.84	1478152.01	871828.95	706398.36	827366.50	1.009874e+07	-0.30
3	2004-01-07	1317907.58	1401310.00	695810.55	1369327.58	1060293.48	359895.93	1499848.87	870903.03	712401.33	820010.32	1.010771e+07	0.09
4	2004-01-08	1269789.53	1356489.71	690412.10	1359515.81	1060453.23	361565.05	1525490.21	869359.32	705817.45	828619.95	1.002751e+07	-0.79
...	...	...	...	...	...	...	...	...	...	...	...	...	...
5028	2023-12-22	40066782.89	82847389.31	8149269.30	24964318.88	3336831.03	1888413.97	11338098.00	18489324.02	1254821.49	4203229.28	1.965385e+08	0.01
5029	2023-12-26	40033994.02	82841992.24	8297177.85	25244271.98	3362382.42	1896674.11	11405151.50	18493274.51	1255263.38	4222325.00	1.970525e+08	0.26
5030	2023-12-27	39888114.62	82804188.02	8367062.35	25267550.44	3351026.28	1899225.16	11473559.61	18464151.55	1264100.13	4225796.46	1.970048e+08	-0.02
5031	2023-12-28	39850641.05	82825792.78	8332260.43	25210091.15	3392665.68	1902018.90	11534518.39	18523876.06	1272053.27	4216248.60	1.970602e+08	0.03
5032	2023-12-29	39922908.88	82048185.92	8298300.55	25248605.75	3397397.56	1903962.59	11520971.17	18561389.84	1272053.27	4239683.64	1.964135e+08	-0.33